Papers › Accurate RGB-D Salient Object Detection via Collaborative Learning
Accurate RGB-D Salient Object Detection via Collaborative Learning
Wei Ji, Jingjing Li, Miao Zhang, Yongri Piao, Huchuan Lu
Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| RGB-D Salient Object Detection | NJU2K | CoNet | Average MAE | 0.047 | #20 of 27 | Archive leaderboard | report |
| RGB-D Salient Object Detection | NJU2K | CoNet | S-Measure | 89.4 | #20 of 27 | Archive leaderboard | report |
| Thermal Image Segmentation | RGB-T-Glass-Segmentation | CoNet | MAE | 0.145 | #21 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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